Here are some AWS services commonly used in data science:

1. Amazon S3 (Simple Storage Service): S3 is an object storage service that allows you to store and retrieve large amounts of data. Data scientists often use S3 as a data lake to store raw data before processing and analysis.

2. Amazon EC2 (Elastic Compute Cloud): EC2 provides scalable virtual machines (instances) that can be used to set up data science environments. You can choose instances with the desired compute and memory capacity for your specific needs.

3. Amazon EMR (Elastic MapReduce): EMR is a cloud-native big data platform that makes it easy to process vast amounts of data using popular frameworks like Hadoop, Spark, and Hive. It’s commonly used for distributed data processing and analysis.

4. Amazon Redshift: Redshift is a fully managed data warehousing service. Data scientists can use it to run complex SQL queries and perform analytics on large datasets, including data stored in S3.

5. AWS Glue: Glue is an ETL (Extract, Transform, Load) service that automates data preparation tasks. It helps data scientists and engineers clean, transform, and prepare data for analysis.

6. Amazon SageMaker: SageMaker is a managed machine learning service that simplifies the process of building, training, and deploying machine learning models. Data scientists can use it to experiment with various algorithms and frameworks.

7. Amazon Quicksight: Quicksight is a business intelligence (BI) tool that allows you to create interactive visualizations and dashboards to explore and share insights from your data.

8. AWS Lambda: Lambda is a serverless computing service that can be used to trigger data processing and analysis tasks in response to events. It’s often used to automate workflows in data pipelines.

9. AWS Step Functions: Step Functions enable you to coordinate multiple AWS services into serverless workflows. Data scientists can use them to orchestrate data processing pipelines.

10. Amazon Kinesis: Kinesis provides services for real-time data streaming and analytics. It’s suitable for applications that require real-time processing of data streams.

11. Amazon Comprehend: Comprehend is a natural language processing (NLP) service that can be used for sentiment analysis, entity recognition, and language detection in text data.

12. AWS Data Pipeline: Data Pipeline is a web service for orchestrating and automating the movement and transformation of data between different AWS services.